Method for sharpening a digital image with signal to noise estimation

ABSTRACT

A method of sharpening a digital image having image pixels according to its noise content, includes the steps of providing an image sharpener having a variable parameter of sharpening; generating a noisy pixel belief map corresponding spatially to the image pixels having belief values indicating the likelihood that the modulation about respective pixels are due to system noise; and using the noisy pixel belief map to vary the parameter of the image sharpener.

FIELD OF THE INVENTION

The invention relates generally to the field of digital image processingand, more particularly, to a method for relating a processing parameterto the contents of an image.

BACKGROUND OF THE INVENTION

In processing a digital image, it is common to sharpen the image andenhance fine detail with sharpening algorithms. Typically, sharpening isperformed by a convolution process (for example, see A. K. Jain,Fundamentals of Digital Image Processing, Prentice-Hall: 1989, pp.249-251). The process of unsharp masking is an example of aconvolution-based sharpening process. For example, sharpening an imagewith unsharp masking can be described by the equation:s(x,y)=i(x,y)**b(x,y)+βf(i(x,y)−i(x,y)**b(x,y))  (0)where:

-   -   s(x,y)=output image with enhanced sharpness    -   i(x,y)=original input image    -   b(x,y)=lowpass filter    -   β=unsharp mask scale factor    -   f( )=fringe function    -   ** denotes two dimensional convolution    -   (x,y) denotes the x^(th) row and the y^(th) column of an image

Typically, an unsharp image is generated by convolution of the imagewith a lowpass filter (i.e., the unsharp image is given byi(x,y)**b(x,y)). Next, the highpass, or fringe data is generated bysubtracting the unsharp image from the original image (i.e., thehighpass data is found with i(x,y)−i(x,y)**b(x,y)). This highpass datais then modified by either a scale factor β or a fringe function f( ) orboth. Finally, the modified highpass data is summed with either theoriginal image or the unsharp image to produce a sharpened image.

A similar sharpening effect can be achieved by modification of the imagein the frequency domain (for example, the FFT domain) as is well knownin the art of digital signal processing.

In U.S. Pat. No. 4,571,635 issued Feb. 18, 1996, Mahmoodi et al. teach amethod of deriving an emphasis coefficient β that is used to scale thehigh frequency information of the digital image depending on thestandard deviation of the image pixels within a neighborhood. Inaddition, in U.S. Pat. No. 5,081,692 issued Jan. 14, 1992, Kwon et alteach that emphasis coefficient β is based on a center weighted variancecalculation. In U.S. Pat. No. 4,761,819 issued Aug. 2, 1988, Denison etal. describes a method where the gain of an unsharp mask is dependent onboth a local variance calculation and a noise statistic.

While these methods do indeed sharpen the image while attempting tominimize noise enhancement, they do not correctly consider the expectednoise from the imaging system and therefore do not provide optimumperformance. For example, none of these methods account for thevariation of expected imaging system noise due signal intensity andimage color.

A need exists therefore for an improved method of predicting the noisein an image for use in an image sharpening algorithm.

SUMMARY OF THE INVENTION

The need is met according to the present invention by providing a methodof sharpening a digital image having image pixels according to its noisecontent, includes the steps of: providing an image sharpener having avariable parameter of sharpening; generating a noisy pixel belief mapcorresponding spatially to the image pixels having belief valuesindicating the likelihood that the modulation about respective pixelsare due to system noise; and using the noisy pixel belief map to varythe parameter of the image sharpener.

ADVANTAGES

The present invention has the advantage that using the noise predictionmethod of the present invention an image can be more optimally sharpenedwithout amplifying noise in the image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a technique for improving animage according to a first embodiment of the invention;

FIG. 2 shows a block diagram of the noise map generator 4;

FIG. 3 shows an example noise table;

FIG. 4 shows a plot of a look-up-table used to convert from the SNR(m,n)image to the noisy pixel belief map N(m,n); and

FIG. 5 is a block diagram of the gain map generator shown in FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, an embodiment of the present inventionwill be described as a method implemented as a software program. Thoseskilled in the art will readily recognize that the equivalent of suchsoftware may also be constructed in hardware. Because image enhancementalgorithms and methods are well known, the present description will bedirected in particular to elements forming part of, or cooperating moredirectly with, the method and system in accordance with the presentinvention. Other elements, and hardware and/or software for producingand otherwise processing the image signals, not specifically shown ordescribed herein, may be selected from such materials, components andelements known in the art. Given the system and method as shown anddescribed according to the invention in the following materials,software not specifically shown, described or suggested herein that isuseful for implementation of the invention is conventional and withinthe ordinary skill in such arts.

Still further, as used herein, the computer program may be stored in acomputer readable storage medium, which may comprise, for example:magnetic storage media such as a magnetic disk (such as a hard drive ora floppy disk) or magnetic tape; optical storage media such as anoptical disc, optical tape, or machine readable bar code; solid stateelectronic storage devices such as random access memory (RAM), or readonly memory (ROM); or any other physical device or medium employed tostore a computer program.

A digital image typically includes one or more two-dimensional arrays ofnumbers. For example, a color digital image may include three arraysrepresenting red, green and blue pixel values respectively, or amonochrome image may include one array of pixel values corresponding tolight intensities. With regard to matters of nomenclature, the value ofa pixel of a digital image located at coordinates (x,y), referring tothe x^(th) row and the y^(th) column of a digital image, shall hereincomprise a triad of values [r(x,y), g(x,y), b(x,y)] respectivelyreferring to the values of the red, green and blue digital imagechannels at location (x,y). In this regard, a digital image may beconsidered as comprising a certain number of digital image channels. Inthe case of a digital image comprising red, green and bluetwo-dimensional arrays, the image comprises three channels, namely, red,green and blue spectral channels.

In general, the present invention describes a method of sharpening animage where the sharpening amount (applied to any local region of theimage) is dependent on both the amount of noise present at the localregion and sharpening target levels related to semantic information inthe local region. As used herein, the term semantic refers to themeaning that would be assigned to a region by an observer. For example,the semantic content of a region may be an object such as a human face,a building, a cloud, or the sky.

Referring to FIG. 1, an image i(x,y) having x_(o) rows and y_(o) columnsis input to a noise map generator 4 for producing a noisy pixel beliefmap N(x,y). Preferably, the image i(x,y) is of high resolution, forexample, an illustrative high resolution image would have x_(o)=1024rows of pixels by y_(o)=1536 columns of pixels. The noisy pixel beliefmap is a map that indicates a belief that the variations in intensityoccurring about a given pixel are due to noise in the imaging systemrather than image content.

Noise in an imaging system is intensity dependent. Therefore at anygiven intensity level, the expected modulation due to noise is known. Ifthe observed modulation is greater than the expected noise modulation,there is a strong belief that the modulation is due to image content. Ifthe observed modulation is less than or equal to the expected noisethere is a strong belief that the modulation is due to noise.Preferably, the belief is expressed as a probability ranging from 0 to100% that a pixel is a noisy pixel. Uniform image areas (e.g. clear bluesky) will generally result in a high belief that the pixels are noisy,while pixels belonging to busy regions (e.g. hair, grass) will generallyresult in low or 0 belief that the pixels are noisy.

The noisy pixel belief map N(x,y) is preferably the same size as theimage i(x,y). Also, the belief values of the noisy pixel belief map arepreferably generated by considering both the local signal variance(measured over the image pixel values) and the expected amount of noise.For example, U.S. Pat. No. 4,761,819, referenced above, describesgenerating a signal G(i,j) which is dependent both on the local varianceV(i,j) and the expected level of noise V(noise). In this way, the noisypixel belief map indicates pixels or regions having high probability ofhaving a low signal to noise ratio (high probability of being a noisypixel). A signal such as G(i,j) could easily be converted to a noisypixel belief map by using a look-up-table (LUT) or linear function as iswell known in the art of image processing. For example, the value of thenoisy pixel belief map could be 0 where G(i,j)>t₁, 100 where G(i,j)<t₂,and attain values intermediate of 0 and 100 when G(i,j) is between t₁and t₂. For example, t₁=5 and t₂=½. Alternatively, European PatentApplication No. 1111906A2, by Gallagher et al., published Jun. 27, 2001,describes a method for generating an expected value of noise is based onthe intensity of the pixel that can be used with a look up table togenerate the noisy pixel belief map

The preferred method for generating the noisy pixel belief map by thenoise map generator 4 is shown in FIG. 2. The image i(x,y) is passed toan image subsampler 22 which generates a low resolution version i(m,n)of the image. The low resolution image i(m,n) contains m₀ rows and n₀columns of pixels, where m₀=x₀/2^(R), n₀=y₀/2^(R) and R is the factor ofsubsampling of the image indicating the number of subsamples by a factorof two that are required. Typically, the low resolution image i(m,n) isgenerated by a combination of filtering and subsampling by a factor of Ras is well known in the art to reduce the effects of aliasing.Generating the noisy pixel belief map from a low resolution image savessubstantial time (as compared with generating the noisy pixel belief mapfrom the image i(x,y)) since there are fewer pixels upon whichcalculations are performed. Preferably, R=2, which saves approximately94% of the processing time required to perform the same operations onthe image i(x,y).

The low resolution image i(m,n) is input to the SNR calculator 24 forcalculating the local signal to noise ratio, SNR(m,n) of the imagechannel to be sharpened at each location. The local signal to noiseratio is represented as a single value at each location (m,n),independent of the number of color channels of the image i(x,y). As willbe described in more detail below, the preferred sharpening is appliedto a single channel of the image, the luminance channel l(x,y). Theluminance channel is created by linearly combining all the colorchannels of the image. For example: $\begin{matrix}{{1\left( {x,y} \right)} = {\sum\limits_{n = 0}^{n = {C - 1}}\quad{a_{n}{c_{n}\left( {x,y} \right)}}}} & (1)\end{matrix}$where:

-   -   C is the number of image channels,    -   c_(n) is the n^(th) color channel of the image i(x,y)    -   a_(n) is the coefficient weighing factor for the n^(th) color        channel. The sum of all the coefficient weighting factors is        preferably 1.0. In the case of an image i(x,y) having red,        green, and blue channels, the preferred values for the red,        green, and blue coefficient weighting factors are all equally ⅓.

In order to calculate the local signal to noise ratio at each pixellocation, the SNR calculator 24 must determine the expected magnitude ofnoise at each pixel location. In the methods of the prior art, theexpected magnitude of noise is calculated only from the channel to besharpened, e.g. the luminance channel. However, it must be recognizedthat many different combinations of pixels values from the variouschannels can form identical luminance channel values through Eq. 1.Therefore, it is very possible that regions of an image may haveidentical mean luminance values, but quite different expected values ofnoise.

The SNR calculator 24 determines the local SNR of the luminance channelwith the following equation: $\begin{matrix}{{{SNR}\left( {m,n} \right)} = {1 + {{{sign}\left\lbrack {{\sigma_{n}\left( {m,n} \right)}^{2} - {\sigma_{k}\left( {i\left( {m,n} \right)} \right)}^{2}} \right\rbrack}\frac{\sqrt{{{\sigma_{n}\left( {m,n} \right)}^{2} - {\sigma_{k}\left( {i\left( {m,n} \right)} \right)}^{2}}}}{\sigma_{k}\left( {i\left( {m,n} \right)} \right)}}}} & (2)\end{matrix}$where:

-   -   σ_(n)(m,n) is the local standard deviation of pixels in the        luminance channel, preferably measured over a 5×5 window        centered at location (m,n).    -   σ_(k)(i(m,n)) is the expected standard deviation of noise of        pixels on the image channel to be sharpened (preferably the        luminance channel of i(m,n)), based on the expected levels of        noise in each color channel of i(m,n).    -   sign[q] is −1 if q<0, 1 otherwise.

The value of σ_(k)(i(m,n)) is a function of several items, including thecoefficient weighting factors used to create the luminance channel, anoise table, the factor of subsampling R, and the pixel values of thesubsampled image i(m,n).

The value of σ_(k)(i(m,n)) is given as: $\begin{matrix}{{\sigma_{k}\left( {i\left( {m,n} \right)} \right)} = {\frac{1}{R_{f}^{R}}\sqrt{\sum\limits_{n = 0}^{n = {C - 1}}\quad{a_{n}^{2}\left( {\sigma_{c_{n}}\left\lbrack {c_{n}\left( {m,n} \right)} \right\rbrack} \right)}^{2}}}} & (3)\end{matrix}$Where:

-   -   R is the number of subsampling levels used by the image        subsampler 22. Preferably R=2.

Rƒ is the ratio by which the standard deviation of noise is reduced ateach resolution level. In the case of uncorrelated noise, Rƒ=2. For realfilm data, Rƒ=1.7 is roughly correct for small values of R (R<=3).

σ_(c) _(n) [q] is the standard deviation of noise at intensity q for theimage color channel c_(n). The noise table, for example as described inEuropean Patent Application No. 1063611A2, by Gallagher et al.,published Dec. 27, 2000 represents this relationship. FIG. 3 shows aplot of a noise table for images having red, green and blue colorchannels for a digital image that was created by scanning a colorphotographic negative. Notice that the noise table represents therelationship between intensity and expected noise magnitude for eachcolor channel at the resolution of the image i(x,y). The term$\frac{1}{R_{f}^{R}}$in Eq. (3) is essentially a correction factor to adjust the fullresolution noise tables to the resolution of the image i(m,n).

The SNR calculator 24 outputs a map SNR(m,n) indicating the signal tonoise ratio at each pixel location of i(m,n). This map is input to thenoisy pixel classifier 26 for converting the signal to a noise ratio mapinto a noisy pixel belief map. This is accomplished by using alook-up-table (LUT) or linear function as is well known in the art ofimage processing. For example, the value of the noisy pixel belief mapcould be 0 where SNR(m,n)>t₃, 100 where SNR(m,n)<t₄, and attain valuesintermediate of 0 and 100 when G(i,j) is between t₃ and t₄. For example,t₃=1.3 and t₄=0.3, as shown in FIG. 4. The noisy pixel belief map isthen interpolated to the resolution of the image i(x,y) by aninterpolator 28.

Those skilled in the art will recognize that certain variations to thenoise map generator 24 may be made with relative ease which will haveonly a small effect on the appearance of the noisy pixel belief map. Forexample, rather than computing the local variance of the luminancechannel in Eq. 2, the local variance of the highpass portion of theluminance channel may be computed. The expected level of noise in thehighpass portion of the luminance channel will be linearly related tothe quantity calculated in Eq. 3. The relationship can be derived fromthe filter used to generate the highpass portion of the luminancechannel, using well-known principles of functions of random variables.

Returning to FIG. 1, the image i(x,y) is passed into the gain mapgenerator 2. The purpose of the gain map generator 2 is to create a mapindicating the gain of the sharpening operation on a pixel-by-pixel orregion-by-region basis based on semantic labels derived from patternrecognition. The gain map is a control signal used to determine thelevel of sharpening to apply on a pixel-by-pixel basis. U.S. Ser. No.10/016,601, filed Dec. 10, 2001 by Luo et al. describes a method bywhich the gain in an unsharp mask is varied based on the semanticcontent of a pixel region. Luo's belief map M(m,n) indicates the beliefthat particular pixels represent certain target subject matter, such asflesh or sky for which a desired level of sharpening has beendetermined.

The belief map is created by a subject matter detector that establishesthe probability that a pixel or region in an image represents a giventarget material. The belief is preferably represented as a probability.For example, each pixel value M(m,n) is equal to 100*P(pixel (m,n) ofthe low resolution image represents the target material), where P(A)represents the probability of event A. Alternatively, each pixel valueM(m,n) may represent a binary classification indicating belief. Forinstance, a pixel value of 1 in the belief map may represent the beliefthat the pixel represents the target subject matter and a pixel value of0 may represent the belief that the pixel does not represent the targetsubject matter. In the preferred embodiment, the target subject matteris human flesh. For example, it is advantageous in terms of imagequality to sharpen human flesh less than other subject matters. Thecontrol signal β(x,y) indicates the gain of an unsharp mask for eachpixel of an input image.

As shown in FIG. 5, the control signal β(x,y) is created by firstapplying one or more subject matter detectors 32 ₁, . . . , 32 _(I) tothe image (or a low resolution version of the image) to create subjectmatter belief maps, then the belief map analyzer 34 combines the beliefmaps with target sharpening levels for each subject matter to producethe gain map control signal β(x,y). The value of the control signalβ(x,y) at any particular location (x,y) is related to the value ofvarious belief maps M(x,y) at the corresponding image locations.Assuming that the size (in lines and columns) of the belief map isidentical to the size of the image, the preferred relationship betweenthe gain map control signal β(x,y) and the belief maps M(x,y) is givenby the equation: $\begin{matrix}{{\beta\left( {x,y} \right)} = {\frac{\sum\limits_{1}\quad\left( {{M_{i}\left( {x,y} \right)}\left( {T_{i} - T_{0}} \right)} \right)}{\max\left( {{\sum\limits_{i}\left( {M_{i}\left( {x,y} \right)} \right)},1} \right)} + T_{0}}} & (4)\end{matrix}$where i represents the index of the subject matter detector. Forexample, M₁(x,y) may be a belief map representing the belief of humanflesh, M₂(x,y) may be a belief map representing belief of blue sky,M₃(x,y) may be a belief map representing the belief of grass, etc.

I_(t) represents the control signal target for a pixel having highbelief in the associated target subject matter. T_(i) is referred to asthe target sharpening levels. Continuing the above example, T₁=0.5 forhuman flesh, T₂=1.0 for blue sky, T₃=3.0 for green grass, etc.

T₀ represents the control signal target for a pixel that is generallyconsidered to be background (“pure” background) by all the subjectmatter detectors. Preferably, T₀=2.75.

Referring back to FIG. 1, the gain map β(x,y) generated by the gain mapgenerator 2 is a control signal that indicates the amount of sharpeningto apply to specific regions or pixels of the digital image i(x,y). Inthe preferred embodiment, the gain map β(x,y) is populated by valuesrepresenting the gain parameter of an unsharp mask sharpening algorithm.The values of the gain map vary on a pixel-by-pixel or region-by-regionbasis depending on the criteria by which the gain map was created by thegain map generator 2. Typically, the values of the gain map β(x,y) varyfrom 0.5 for pixels with high belief of representing flesh and 3 0 forpixels with high belief of representing sky and 2.75 for backgroundpixels. The gain map generator 2 does not consider the noise content ofthe image when generating the gain map.

Both the noise map generator 4 and the gain map generator 2 can operateon a low resolution version of the image i(x,y) in order to reduce thecomputational cost.

The noise map N(x,y) from the noise map generator 4 and the gain mapβ(x,y) from the gain map generator 2 are input to the gain map modifier6. The purpose of the gain map modifier 6 is to modify the gain map sothat the gain does not exceed a predetermined limit in areas where thenoisy pixel belief map indicates high belief that the pixel or region isnoisy. To this end, the gain map modifier also inputs a noise sharpeninglimit N_(sl), which is a parameter indicating a maximum level ofsharpening for noisy pixels. In the preferred embodiment, the noisesharpening limit N_(sl) is the maximum gain of an unsharp mask for thosepixels having high belief in the noisy pixel belief map N(x,y).Preferably N_(sl)=1.3. The noise sharpening limit is not the desiredsharpening level for all noisy pixels, because some pixels which areindicated as noisy pixels in the noisy pixel belief map may also alreadyhave sharpening levels in the gain map lower than the noise sharpeninglimit. The noise sharpening limit only affects the gain values of pixelswhen the following two conditions are met:

A. The gain value of the pixel in the gain map β(x,y) is greater thanthe noise sharpening limit N_(sl)

B. The pixel has non-zero belief that the pixel is a noisy pixel. Thefinal gain map β_(n)(x,y) which considers the image noise is output fromthe gain map modifier 6. The final gain map is generated with thefollowing equation, which meets the requirements set forth in A. and B.above:β_(n)(x,y)=min[β(x,y),N _(sl) ]+N(x,y)·(max[β(x,y),N _(sl) ]−N_(sl))  (5)where

-   -   min(β(x,y),N_(sl)) is an image with the same number of rows and        columns as β(x,y). The image is identical to β(x,y) for all        pixels greater than N_(sl) and is equal to N_(sl) elsewhere.    -   max(β(x,y),N_(sl)) is an image with the same number of rows and        columns as β(x,y). The image is identical to β(x,y) for all        pixels less than N_(sl) and is equal to N_(sl) elsewhere.

The final gain map β_(n)(x,y) determined by the gain map modifier 6 isthen input to the image sharpener 10. While in the present embodiment ofthe invention the final gain map β_(n)(x,y) is the scale factor of anunsharp mask, the function of the gain map modifier 6 is not limited tothe use of a scale factor and other sharpness related determinationscould be used. For example, the filter used in the sharpeningconvolution performed by the image sharpener 10 could be determined bythe gain map modifier 6 based on an analysis of the belief map.

The image i(x,y) and the final gain map β_(n)(x,y) are passed to theimage sharpener for sharpening the image according the final gain mapβ_(n)(x,y).

The image sharpener 10 inputs the sharpening parameter(s) and applies asharpening algorithm to the image, utilizing the sharpening parameter(s)in order to produce an enhanced output image having improved sharpnesswithout producing objectionable sharpness artifacts. In the preferredembodiment, the sharpener 10 applies an unsharp masking algorithm to theimage using the final gain map β_(n)(x,y) in order to produce theenhanced image, as is described in an equation below. For example,sharpening an image with an unsharp mask can be described with thefollowing equation:s(x,y)=i(x,y)**b(m,n)+β_(n)(x,y)ƒ(i(x,y)−i(x,y)**b(m,n))  (6)where

-   -   s(x,y)=output image with enhanced sharpness    -   i(x,y)=original input image    -   b(m,n)=lowpass convolution filter (preferably a Gaussian lowpass        filter with a size of 1 pixel per standard deviation. The filter        coefficients of a 5×5 filter are as follows:    -   [0.003 0.0133 0.0219 0.0133 0.003    -   0.0133 0.0596 0.0983 0.0596 0.0133    -   0.0219 0.0983 0.162 0.0983 0.0219    -   0.0133 0.0596 0.0983 0.0596 0.0133 0.003 0.0133 0.0219 0.0133        0.003])    -   β_(n)(x,y)=final gain map    -   ƒ(x,y)=fringe function    -   ** denotes two dimensional convolution    -   (x,y) denotes the x^(th) row and the y^(th) column of an image    -   (m,n) denotes the m^(th) row and the n^(th) column of the        convolution filter

Those skilled in the art will recognize that there are several methodsby which unsharp masking (such as provided by Eq. (1)) can be applied toa color image having multiple channels. For example, the unsharp maskprocess can be applied to each channel of the color image. Preferably,the unsharp mask process is applied in the following manner.

Assuming the input image is a color image consisting of red, green, andblue color channels, a matrix is first applied to the image in order toproduce a luminance channel and two or more color difference channels.Next the unsharp mask process is applied to the luminance channel.Finally, an inverse matrix is applied to the luminance and colordifference channels to generate an enhanced color image having red greenand blue channels.

Alternatively, the unsharp mask process may be applied to only a singleimage channel (e.g. the green channel), and the modified highpass datamay be summed with each color channel in order to generate an enhancedcolor image. These and other similar modifications and enhancements tothe unsharp mask process would be well understood by those of skill inthis art. Since the particularities of their usage are not fundamentallyrelated to the method of selecting sharpening parameters for thevariable gain sharpening, their particular application does not act toin any way limit the scope of the invention.

Those skilled in the art will also recognize that although Eq. (6) andthe present invention generally describe the sharpening applied to theimage as being performed by an unsharp mask, that is not necessarily thecase. Assuming the fringe function ƒ( ) of Eq. (6) is identity, theunsharp mask process can be reconfigured as a single filter than can beapplied with convolution to the image and produce results identical tothe unsharp mask. For example, suppose the filter coefficients of b(x,y)are given as: ${b\left( {x,y} \right)} = {\frac{\begin{bmatrix}1 & 2 & 1 \\2 & 4 & 2 \\1 & 2 & 1\end{bmatrix}}{16}.}$Application of a filter c(x,y) with a convolution having coefficientsgiven as: ${c\left( {x,y} \right)} = \frac{\begin{bmatrix}{1 - \beta} & {2\left( {1 - \beta} \right)} & {1 - \beta} \\{2\left( {1 - \beta} \right)} & {4\left( {1 + {3\beta}} \right)} & {2\left( {1 - \beta} \right)} \\{1 - \beta} & {2\left( {1 - \beta} \right)} & {1 - \beta}\end{bmatrix}}{16}$will produce identical results compared with using filter b(x,y) in theunsharp mask of Eq. (1). Such modifications to the preferred embodimentby the grouping of operations in the image sharpener 10 such as can bedetermined by methods well known in algebra and digital signalprocessing will be evident to those of skill in this art and are withinthe scope of the present invention. Notice that in each case, thecoefficients of the filter are independent of the pixel valuessurrounding location (x,y).

The present invention has been described with reference to a preferredembodiment. Changes may be made to the preferred embodiment withoutdeviating from the scope of the present invention.

PARTS LIST 2 Gain map generator 4 noise map generator 6 gain mapmodifier 10 image sharpener 22 image subsampler 24 SNR calculator 26noisy pixel classifier 28 image interpolator 32 subject matter detector34 belief map analyzer

1. A method of sharpening a digital image having image pixels accordingto its noise content, comprising the steps of: generating a noisy pixelbelief map corresponding spatially to the image pixels having beliefvalues indicating the likelihood that the modulation about respectivepixels are due to system noise, said noisy pixel belief map being basedupon both a local noise measure of pixels of the digital image and anoise table separate from said digital image; and using the noisy pixelbelief map to vary a variable parameter of an image sharpener; whereinthe step of generating a noisy pixel belief map comprises the steps of;creating a low resolution version of the digital image; generating a lowresolution noisy pixel belief map from the low resolution version of thedigital image; and interpolating the low resolution noisy pixel beliefmap to produce the noisy pixel belief map.
 2. The method claimed inclaim 1, wherein said local noise measure is local variance of thepixels and said noise table indicates expected variance of noise of thepixels.
 3. The method claimed in claim 2, further comprising subsamplingsaid digital image to a predetermined number of subsampling levels. 4.The method claimed in claim 3, wherein the digital image includes two ormore channels, and the step of generating a noisy pixel belief mapcomprises the steps of: calculating a signal to noise ratio for at leastone pixel of the digital image, the signal to noise ratio based on: saidlocal variance of the pixels, said noise table, and said number ofsubsampling levels; and computing a belief value of the noisy pixelbelief map from the signal to noise ratio.
 5. The method claimed inclaim 1, wherein the digital image is a color digital image having twoor more channels and including the steps of forming a luminance channelas a weighted sum of the two or more channels; and applying the imagesharpener to the luminance channel.
 6. The method claimed in claim 5,wherein the noisy pixel belief map is generated using weightingcoefficients employed in the weighted sum.
 7. The method claimed inclaim 6, wherein the step of generating a noisy pixel belief mapcomprises the steps of: providing a noise table indicating therelationship, for each channel of the digital image, between pixelintensity and expected noise magnitude; calculating a signal to noiseratio for at least one pixel of the digital image, the signal to noiseratio based on the noise table weighted by a corresponding weightingcoefficient; and computing a belief value of the noisy pixel belief mapfrom the signal to noise ratio.
 8. A method of sharpening a digitalimage having image pixels according to its noise content, comprising thesteps of: a) providing an image sharpener having a variable parameter ofsharpening; b) generating a noisy pixel belief map correspondingspatially to the image pixels having belief values indicating thelikelihood that the modulation about respective pixels are due to systemnoise; and c) using the noisy pixel belief map to vary the parameter ofthe image sharpener; wherein the step of generating a noisy pixel beliefmap comprises the steps of: b1) creating a low resolution version of thedigital image; b2) generating a low resolution noisy pixel belief mapfrom the low resolution version of the digital image; and b3)interpolating the low resolution noisy pixel belief map to produce thenoisy pixel belief map.
 9. The method claimed in claim 8, wherein saidnoisy pixel belief map is based upon both a local noise measure ofpixels of the digital image and a noise table independent of saiddigital image.
 10. The method claimed in claim 8, wherein said localnoise measure is local variance of the pixels and said noise tableindicates expected variance of noise of the pixels.
 11. The methodclaimed in claim 10, further comprising subsampling said digital imageto a predetermined number of subsampling levels.
 12. The method claimedin claim 11, wherein the digital image includes two or more channels,and the step of generating a noisy pixel belief map comprises the stepsof: calculating a signal to noise ratio for at least one pixel of thedigital image, the signal to noise ratio based on: said local varianceof the pixels, said noise table, and said number of subsampling levels;and computing a belief value of the noisy pixel belief map from thesignal to noise ratio.
 13. The method claimed in claim 8, wherein thedigital image is a color digital image having two or more channels andincluding the steps of forming a luminance channel as a weighted sum ofthe two or more channels; and applying the image sharpener to theluminance channel.
 14. The method claimed in claim 13, wherein the noisypixel belief map is generated using weighting coefficients employed inthe weighted sum and the step of generating a noisy pixel belief mapcomprises the steps of: providing a noise table indicating therelationship, for each channel of the digital image, between pixelintensity and expected noise magnitude; calculating a signal to noiseratio for at least one pixel of the digital image, the signal to noiseratio based on the noise table weighted by a corresponding weightingcoefficient; and computing a belief value of the noisy pixel belief mapfrom the signal to noise ratio.
 15. A method of sharpening a digitalimage having image pixels according to its noise content, comprising thesteps of: subsampling said digital image to a predetermined number ofsubsampling levels to provide a subsampled image; generating a noisypixel belief map from said subsampled image, said noisy pixel belief mapbeing based upon a local noise measure of pixels of the digital image, anoise table separate from said digital image, and said number ofsubsampling levels; using the noisy pixel belief map to vary a variableparameter of an image sharpener; and applying said image sharpener tosaid digital image; wherein said generating further comprises:generating a low resolution noisy pixel belief map from said subsampledimage; and interpolating said low resolution noisy pixel belief map toproduce said noisy pixel belief map.
 16. The method claimed in claim 15,wherein said local noise measure is local variance of the pixels andsaid noise table indicates expected variance of noise of the pixels. 17.The method claimed in claim 15, wherein said noisy pixel belief map mapssignal to noise ratios of pixels of the subsampled image, said signal tonoise ratios being represented by the equation: $\begin{matrix}{{{SNR}\left( {m,n} \right)} = {1 + {{sign}\left\lbrack {{\sigma_{n}\left( {m,n} \right)}^{2} -} \right.}}} \\{\left. {\sigma_{k}\left( {i\left( {m,n} \right)} \right)}^{2} \right\rbrack\frac{\sqrt{{{\sigma_{n}\left( {m,n} \right)}^{2} - {\sigma_{k}\left( {i\left( {m,n} \right)} \right)}^{2}}}}{\sigma_{k}\left( {i\left( {m,n} \right)} \right)}}\end{matrix}$ wherein: SNR(m,n) is the signal to noise ratio; m,n arethe coordinates of each pixel; σ_(n) (m,n) is a standard deviation ofpixels of a luminance channel in a window centered on m,n, saidluminance channel being a linear combination of a plurality of colorchannels; sign[q] is −1 if q<0, otherwise q=1; and${\sigma_{k}\left( {i\left( {m,n} \right)} \right)} = {\frac{1}{R_{f}^{R}}\sqrt{\sum\limits_{n = 0}^{n = {C - 1}}\quad{a_{n}^{2}\left( {\sigma_{c_{n}}\left\lbrack {c_{n}\left( {m,n} \right)} \right\rbrack} \right)}^{2}}}$for each of said color channels, wherein: R is the number of subsamplinglevels; σ_(c) _(n) [q] is a standard deviation of noise at intensity qfor one of the color channels c_(n); R_(ƒ)is a constant.
 18. The methodclaimed in claim 17 wherein R_(ƒ)is 1.7 and R is less than or equal to3.